26 Jan 2022
26 Jan 2022
Status: a revised version of this preprint is currently under review for the journal HESS.

Seamless streamflow model provides forecasts at all scales from daily to monthly and matches the performance of non-seamless monthly model

David McInerney1, Mark Thyer1, Dmitri Kavetski1, Richard Laugesen2, Fitsum Woldemeskel3, Narendra Tuteja2, and George Kuczera4 David McInerney et al.
  • 1School of Civil, Environmental and Mining Engineering, University of Adelaide, SA, Australia
  • 2Bureau of Meteorology, ACT, Canberra, Australia
  • 3Bureau of Meteorology, VIC, Melbourne, Australia
  • 4School of Engineering, University of Newcastle, Callaghan, NSW, Australia

Abstract. Subseasonal streamflow forecasts inform a multitude of water management decisions, from early flood warning to reservoir operation. ‘Seamless’ forecasts, i.e., forecasts that are reliable over a range of lead times (1–30 days) and when aggregated to multiples time scales (e.g. daily and monthly) are of clear practical interest. However, existing forecasting products are often ‘non-seamless’, i.e., designed for a single time scale and lead time (e.g. 1 month ahead). If seamless forecasts are to be a viable replacement for existing ‘non-seamless’ forecasts, it is important that they offer (at least) similar predictive performance at the time scale of the non-seamless forecast.

This study compares the recently developed seamless daily Multi-Temporal Hydrological Residual Error (MuTHRE) model to the (non-seamless) monthly streamflow post-processing (QPP) model that was used in the Australian Bureau of Meteorology’s Dynamic Forecasting System. Streamflow forecasts from both models are generated for 11 Australian catchments, using the GR4J hydrological model and post-processed rainfall forecasts from the ACCESS-S climate model. Evaluating monthly forecasts with key performance metrics (reliability, sharpness, bias and CRPS skill score), we find that the seamless MuTHRE model provides essentially the same performance as the non-seamless monthly QPP model for the vast majority of metrics and temporal stratifications (months and years). When this outcome is combined with the numerous practical benefits of seamless forecasts it is clear that seamless forecasting technologies, such as the MuTHRE model, are not only viable, but a preferred choice for future research development and practical adoption of streamflow forecasting.

David McInerney et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-589', Anonymous Referee #1, 02 May 2022
    • AC1: 'Reply on RC1', David McInerney, 02 Jun 2022
  • RC2: 'Comment on hess-2021-589', Anonymous Referee #2, 04 May 2022
    • AC2: 'Reply on RC2', David McInerney, 02 Jun 2022

David McInerney et al.

David McInerney et al.


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Short summary
Forecasts of streamflow a day to a month ahead are highly valuable for water management. Current practice often employs models developed for specific lead times. In contrast, a "seamless" forecast model is intended to serve multiple lead times. This study shows that the seamless model matches the performance of a model tuned specifically for monthly predictions – while providing forecasts at other lead times. This finding paves the way for wider practical adoption of seamless forecast models.